One of the purported advantages of three-dimensional (“3D”) face recognition is the ability to handle large variations in pose (e.g., orientation) of a subject. This advantage is attributed to the consistent 3D geometric structure that is exhibited between facial scans acquired from significantly different angles. However, several problems have to be addressed to achieve robust 3D face recognition performance in a statistical learning framework across a variety of poses. Statistical learning based algorithms require a stringent standardization whereby a consistent pose, a consistent number of points and consistent facial regions are represented across faces. This process is complicated when face recognition is needed for faces acquired from significantly different views due to the following: 1) 3D facial scans, acquired using a 3D camera system from a largely non-frontal view, will exhibit missing regions or holes compared to the frontal view due to object self-occlusion; and 2) 3D face alignment, of two scans acquired from dramatically different angles, is complicated by the large angle variation and the missing regions between the two significantly different views.
What is needed is an improved system and method for obtaining standardized 3D face representations from non-frontal face views for a statistical learning algorithm.